Overview

Dataset statistics

Number of variables63
Number of observations266
Missing cells15809
Missing cells (%)94.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.5 KiB
Average record size in memory617.9 B

Variable types

Categorical7
Unsupported40
Numeric16

Alerts

1998 has constant value "2.72573725747135" Constant
1999 has constant value "22.9505243775518" Constant
2000 has constant value "17.3611900799107" Constant
Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
2004 is highly correlated with 2009 and 4 other fieldsHigh correlation
2005 is highly correlated with 2006 and 9 other fieldsHigh correlation
2006 is highly correlated with 2005 and 13 other fieldsHigh correlation
2007 is highly correlated with 2005 and 13 other fieldsHigh correlation
2008 is highly correlated with 2005 and 13 other fieldsHigh correlation
2009 is highly correlated with 2004 and 14 other fieldsHigh correlation
2010 is highly correlated with 2004 and 14 other fieldsHigh correlation
2011 is highly correlated with 2005 and 13 other fieldsHigh correlation
2012 is highly correlated with 2004 and 13 other fieldsHigh correlation
2013 is highly correlated with 2004 and 14 other fieldsHigh correlation
2014 is highly correlated with 2005 and 13 other fieldsHigh correlation
2015 is highly correlated with 2005 and 13 other fieldsHigh correlation
2016 is highly correlated with 2004 and 14 other fieldsHigh correlation
2017 is highly correlated with 2006 and 12 other fieldsHigh correlation
2018 is highly correlated with 2006 and 12 other fieldsHigh correlation
2019 is highly correlated with 2006 and 12 other fieldsHigh correlation
2004 is highly correlated with 2009 and 4 other fieldsHigh correlation
2005 is highly correlated with 2006 and 8 other fieldsHigh correlation
2006 is highly correlated with 2005 and 13 other fieldsHigh correlation
2007 is highly correlated with 2005 and 13 other fieldsHigh correlation
2008 is highly correlated with 2005 and 13 other fieldsHigh correlation
2009 is highly correlated with 2004 and 14 other fieldsHigh correlation
2010 is highly correlated with 2004 and 14 other fieldsHigh correlation
2011 is highly correlated with 2005 and 13 other fieldsHigh correlation
2012 is highly correlated with 2004 and 13 other fieldsHigh correlation
2013 is highly correlated with 2004 and 14 other fieldsHigh correlation
2014 is highly correlated with 2005 and 13 other fieldsHigh correlation
2015 is highly correlated with 2005 and 13 other fieldsHigh correlation
2016 is highly correlated with 2004 and 13 other fieldsHigh correlation
2017 is highly correlated with 2006 and 12 other fieldsHigh correlation
2018 is highly correlated with 2006 and 12 other fieldsHigh correlation
2019 is highly correlated with 2006 and 12 other fieldsHigh correlation
2004 is highly correlated with 2009 and 4 other fieldsHigh correlation
2005 is highly correlated with 2006 and 9 other fieldsHigh correlation
2006 is highly correlated with 2005 and 12 other fieldsHigh correlation
2007 is highly correlated with 2005 and 13 other fieldsHigh correlation
2008 is highly correlated with 2005 and 13 other fieldsHigh correlation
2009 is highly correlated with 2004 and 14 other fieldsHigh correlation
2010 is highly correlated with 2004 and 14 other fieldsHigh correlation
2011 is highly correlated with 2005 and 13 other fieldsHigh correlation
2012 is highly correlated with 2004 and 13 other fieldsHigh correlation
2013 is highly correlated with 2004 and 14 other fieldsHigh correlation
2014 is highly correlated with 2005 and 13 other fieldsHigh correlation
2015 is highly correlated with 2005 and 13 other fieldsHigh correlation
2016 is highly correlated with 2004 and 14 other fieldsHigh correlation
2017 is highly correlated with 2007 and 11 other fieldsHigh correlation
2018 is highly correlated with 2006 and 12 other fieldsHigh correlation
2019 is highly correlated with 2006 and 12 other fieldsHigh correlation
2005 is highly correlated with 2009 and 5 other fieldsHigh correlation
2006 is highly correlated with 2007 and 10 other fieldsHigh correlation
2007 is highly correlated with 2006 and 12 other fieldsHigh correlation
2008 is highly correlated with 2006 and 12 other fieldsHigh correlation
2009 is highly correlated with 2005 and 12 other fieldsHigh correlation
2010 is highly correlated with 2005 and 13 other fieldsHigh correlation
2011 is highly correlated with 2006 and 12 other fieldsHigh correlation
2012 is highly correlated with 2005 and 13 other fieldsHigh correlation
2013 is highly correlated with 2006 and 12 other fieldsHigh correlation
2014 is highly correlated with 2005 and 13 other fieldsHigh correlation
2015 is highly correlated with 2005 and 12 other fieldsHigh correlation
2016 is highly correlated with 2005 and 13 other fieldsHigh correlation
2017 is highly correlated with 2006 and 12 other fieldsHigh correlation
2018 is highly correlated with 2006 and 12 other fieldsHigh correlation
2019 is highly correlated with 2006 and 12 other fieldsHigh correlation
1960 has 266 (100.0%) missing values Missing
1961 has 266 (100.0%) missing values Missing
1962 has 266 (100.0%) missing values Missing
1963 has 266 (100.0%) missing values Missing
1964 has 266 (100.0%) missing values Missing
1965 has 266 (100.0%) missing values Missing
1966 has 266 (100.0%) missing values Missing
1967 has 266 (100.0%) missing values Missing
1968 has 266 (100.0%) missing values Missing
1969 has 266 (100.0%) missing values Missing
1970 has 266 (100.0%) missing values Missing
1971 has 266 (100.0%) missing values Missing
1972 has 266 (100.0%) missing values Missing
1973 has 266 (100.0%) missing values Missing
1974 has 266 (100.0%) missing values Missing
1975 has 266 (100.0%) missing values Missing
1976 has 266 (100.0%) missing values Missing
1977 has 266 (100.0%) missing values Missing
1978 has 266 (100.0%) missing values Missing
1979 has 266 (100.0%) missing values Missing
1980 has 266 (100.0%) missing values Missing
1981 has 266 (100.0%) missing values Missing
1982 has 266 (100.0%) missing values Missing
1983 has 266 (100.0%) missing values Missing
1984 has 266 (100.0%) missing values Missing
1985 has 266 (100.0%) missing values Missing
1986 has 266 (100.0%) missing values Missing
1987 has 266 (100.0%) missing values Missing
1988 has 266 (100.0%) missing values Missing
1989 has 266 (100.0%) missing values Missing
1990 has 266 (100.0%) missing values Missing
1991 has 266 (100.0%) missing values Missing
1992 has 266 (100.0%) missing values Missing
1993 has 266 (100.0%) missing values Missing
1994 has 266 (100.0%) missing values Missing
1995 has 266 (100.0%) missing values Missing
1996 has 266 (100.0%) missing values Missing
1997 has 266 (100.0%) missing values Missing
1998 has 265 (99.6%) missing values Missing
1999 has 265 (99.6%) missing values Missing
2000 has 265 (99.6%) missing values Missing
2001 has 266 (100.0%) missing values Missing
2002 has 263 (98.9%) missing values Missing
2003 has 264 (99.2%) missing values Missing
2004 has 260 (97.7%) missing values Missing
2005 has 253 (95.1%) missing values Missing
2006 has 247 (92.9%) missing values Missing
2007 has 245 (92.1%) missing values Missing
2008 has 238 (89.5%) missing values Missing
2009 has 237 (89.1%) missing values Missing
2010 has 234 (88.0%) missing values Missing
2011 has 232 (87.2%) missing values Missing
2012 has 227 (85.3%) missing values Missing
2013 has 242 (91.0%) missing values Missing
2014 has 236 (88.7%) missing values Missing
2015 has 232 (87.2%) missing values Missing
2016 has 235 (88.3%) missing values Missing
2017 has 240 (90.2%) missing values Missing
2018 has 239 (89.8%) missing values Missing
2019 has 250 (94.0%) missing values Missing
2020 has 266 (100.0%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
2002 is uniformly distributed Uniform
2003 is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique
1960 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1961 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1962 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1963 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1964 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1965 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1966 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1967 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1968 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1969 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1970 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1971 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1972 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1973 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1974 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1975 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1976 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1977 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1978 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1979 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1980 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1981 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1982 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1983 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1984 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1985 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1986 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1987 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1988 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1989 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1990 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1991 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1992 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1993 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1994 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1995 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1996 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1997 is an unsupported type, check if it needs cleaning or further analysis Unsupported
2001 is an unsupported type, check if it needs cleaning or further analysis Unsupported
2020 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-04-02 19:28:01.563047
Analysis finished2022-04-02 19:28:27.297873
Duration25.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Aruba
 
1
Oman
 
1
Malawi
 
1
Malaysia
 
1
North America
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAruba
2nd rowAfrica Eastern and Southern
3rd rowAfghanistan
4th rowAfrica Western and Central
5th rowAngola

Common Values

ValueCountFrequency (%)
Aruba1
 
0.4%
Oman1
 
0.4%
Malawi1
 
0.4%
Malaysia1
 
0.4%
North America1
 
0.4%
Namibia1
 
0.4%
New Caledonia1
 
0.4%
Niger1
 
0.4%
Nigeria1
 
0.4%
Nicaragua1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T14:28:27.384612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
islands9
 
1.8%
africa9
 
1.8%
ibrd8
 
1.6%
asia8
 
1.6%
countries7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
ABW
 
1
OMN
 
1
MWI
 
1
MYS
 
1
NAC
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowABW
2nd rowAFE
3rd rowAFG
4th rowAFW
5th rowAGO

Common Values

ValueCountFrequency (%)
ABW1
 
0.4%
OMN1
 
0.4%
MWI1
 
0.4%
MYS1
 
0.4%
NAC1
 
0.4%
NAM1
 
0.4%
NCL1
 
0.4%
NER1
 
0.4%
NGA1
 
0.4%
NIC1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-02T14:28:27.490359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abw1
 
0.4%
aut1
 
0.4%
btn1
 
0.4%
brn1
 
0.4%
afg1
 
0.4%
afw1
 
0.4%
ago1
 
0.4%
alb1
 
0.4%
and1
 
0.4%
arb1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1960
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1961
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1962
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1963
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1964
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1965
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1966
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1967
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1968
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1969
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1970
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1971
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1972
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1973
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1974
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1975
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1976
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1977
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1978
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1979
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1980
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1981
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1982
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1983
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1984
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1985
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1986
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1987
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1988
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1989
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1990
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1991
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1992
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1993
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1994
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1995
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1996
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1997
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1998
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
2.72573725747135

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2.72573725747135

Common Values

ValueCountFrequency (%)
2.725737257471351
 
0.4%
(Missing)265
99.6%

Length

2022-04-02T14:28:27.581116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-02T14:28:27.627963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2.725737257471351
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1999
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
22.9505243775518

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row22.9505243775518

Common Values

ValueCountFrequency (%)
22.95052437755181
 
0.4%
(Missing)265
99.6%

Length

2022-04-02T14:28:27.938131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-02T14:28:27.998996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
22.95052437755181
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

2000
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
17.3611900799107

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row17.3611900799107

Common Values

ValueCountFrequency (%)
17.36119007991071
 
0.4%
(Missing)265
99.6%

Length

2022-04-02T14:28:28.059835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-02T14:28:28.119645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
17.36119007991071
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

2001
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

2002
Categorical

MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
20.6152424724155
13.3701014548432
5.56863514485645

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row20.6152424724155
2nd row13.3701014548432
3rd row5.56863514485645

Common Values

ValueCountFrequency (%)
20.61524247241551
 
0.4%
13.37010145484321
 
0.4%
5.568635144856451
 
0.4%
(Missing)263
98.9%

Length

2022-04-02T14:28:28.181532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-02T14:28:28.283281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
20.61524247241551
33.3%
13.37010145484321
33.3%
5.568635144856451
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

2003
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing264
Missing (%)99.2%
Memory size10.6 KiB
6.62698090721438
8.19379853906782

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row6.62698090721438
2nd row8.19379853906782

Common Values

ValueCountFrequency (%)
6.626980907214381
 
0.4%
8.193798539067821
 
0.4%
(Missing)264
99.2%

Length

2022-04-02T14:28:28.359053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-02T14:28:28.420916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
6.626980907214381
50.0%
8.193798539067821
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

2004
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)100.0%
Missing260
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean15.36670104
Minimum0.9739795895
Maximum53.66088287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:28.474766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.9739795895
5-th percentile1.108573706
Q11.604345073
median3.321299353
Q323.2483634
95-th percentile47.5982594
Maximum53.66088287
Range52.68690328
Interquartile range (IQR)21.64401833

Descriptive statistics

Standard deviation21.71220455
Coefficient of variation (CV)1.412938567
Kurtosis1.041264115
Mean15.36670104
Median Absolute Deviation (MAD)2.078131531
Skewness1.454924539
Sum92.20020623
Variance471.4198263
MonotonicityNot monotonic
2022-04-02T14:28:28.768122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1.5123560551
 
0.4%
1.8803121281
 
0.4%
53.660882871
 
0.4%
4.7622865771
 
0.4%
0.97397958951
 
0.4%
29.410389011
 
0.4%
(Missing)260
97.7%
ValueCountFrequency (%)
0.97397958951
0.4%
1.5123560551
0.4%
1.8803121281
0.4%
4.7622865771
0.4%
29.410389011
0.4%
53.660882871
0.4%
ValueCountFrequency (%)
53.660882871
0.4%
29.410389011
0.4%
4.7622865771
0.4%
1.8803121281
0.4%
1.5123560551
0.4%
0.97397958951
0.4%

2005
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct13
Distinct (%)100.0%
Missing253
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean11.5715674
Minimum0.7725058208
Maximum48.39835359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:28.853864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7725058208
5-th percentile0.8868686092
Q11.647646353
median3.661218287
Q313.4239636
95-th percentile37.9711829
Maximum48.39835359
Range47.62584777
Interquartile range (IQR)11.77631725

Descriptive statistics

Standard deviation14.73783566
Coefficient of variation (CV)1.273624838
Kurtosis2.167265111
Mean11.5715674
Median Absolute Deviation (MAD)2.888712467
Skewness1.640861737
Sum150.4303762
Variance217.2037998
MonotonicityNot monotonic
2022-04-02T14:28:28.944899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1.6476463531
 
0.4%
8.914796811
 
0.4%
13.42396361
 
0.4%
3.6612182871
 
0.4%
1.907167091
 
0.4%
25.42971431
 
0.4%
48.398353591
 
0.4%
0.77250582081
 
0.4%
1.2098038011
 
0.4%
31.019735781
 
0.4%
Other values (3)3
 
1.1%
(Missing)253
95.1%
ValueCountFrequency (%)
0.77250582081
0.4%
0.96311046811
0.4%
1.2098038011
0.4%
1.6476463531
0.4%
1.907167091
0.4%
2.808162951
0.4%
3.6612182871
0.4%
8.914796811
0.4%
10.274197331
0.4%
13.42396361
0.4%
ValueCountFrequency (%)
48.398353591
0.4%
31.019735781
0.4%
25.42971431
0.4%
13.42396361
0.4%
10.274197331
0.4%
8.914796811
0.4%
3.6612182871
0.4%
2.808162951
0.4%
1.907167091
0.4%
1.6476463531
0.4%

2006
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)100.0%
Missing247
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean22.43249027
Minimum3.286746257
Maximum50.74009351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:29.027679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.286746257
5-th percentile4.557244375
Q19.166794669
median22.33809534
Q330.60805245
95-th percentile48.20916553
Maximum50.74009351
Range47.45334725
Interquartile range (IQR)21.44125778

Descriptive statistics

Standard deviation14.10059032
Coefficient of variation (CV)0.6285789116
Kurtosis-0.5413008475
Mean22.43249027
Median Absolute Deviation (MAD)9.541660053
Skewness0.4221594482
Sum426.2173151
Variance198.8266473
MonotonicityNot monotonic
2022-04-02T14:28:29.118487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
22.338095341
 
0.4%
17.159882451
 
0.4%
10.241214591
 
0.4%
50.740093511
 
0.4%
30.75010411
 
0.4%
14.684392661
 
0.4%
47.927951311
 
0.4%
36.422922981
 
0.4%
7.4725340291
 
0.4%
3.2867462571
 
0.4%
Other values (9)9
 
3.4%
(Missing)247
92.9%
ValueCountFrequency (%)
3.2867462571
0.4%
4.6984108331
0.4%
6.293173311
0.4%
7.4725340291
0.4%
8.0923747441
0.4%
10.241214591
0.4%
14.684392661
0.4%
17.159882451
0.4%
20.581597361
0.4%
22.338095341
0.4%
ValueCountFrequency (%)
50.740093511
0.4%
47.927951311
0.4%
36.422922981
0.4%
31.879755391
0.4%
30.75010411
0.4%
30.46600081
0.4%
30.1009391
0.4%
28.185387961
0.4%
24.895738521
0.4%
22.338095341
0.4%

2007
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)100.0%
Missing245
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean20.839482
Minimum0.5390368797
Maximum57.12081071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:29.207218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5390368797
5-th percentile0.7516148383
Q12.804667869
median6.815692683
Q341.52379123
95-th percentile55.15486174
Maximum57.12081071
Range56.58177383
Interquartile range (IQR)38.71912336

Descriptive statistics

Standard deviation21.13509875
Coefficient of variation (CV)1.014185417
Kurtosis-1.541466471
Mean20.839482
Median Absolute Deviation (MAD)6.064077845
Skewness0.5250399151
Sum437.6291219
Variance446.692399
MonotonicityNot monotonic
2022-04-02T14:28:29.295008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.53903687971
 
0.4%
32.379277171
 
0.4%
55.154861741
 
0.4%
5.4084113061
 
0.4%
41.865004531
 
0.4%
5.0795265311
 
0.4%
46.971331541
 
0.4%
6.8156926831
 
0.4%
40.535767351
 
0.4%
0.75161483831
 
0.4%
Other values (11)11
 
4.1%
(Missing)245
92.1%
ValueCountFrequency (%)
0.53903687971
0.4%
0.75161483831
0.4%
1.2092781191
0.4%
1.430149041
0.4%
1.6525262981
0.4%
2.8046678691
0.4%
5.0795265311
0.4%
5.4084113061
0.4%
5.4285896381
0.4%
6.0006455181
0.4%
ValueCountFrequency (%)
57.120810711
0.4%
55.154861741
0.4%
47.715361911
0.4%
46.971331541
0.4%
41.865004531
0.4%
41.523791231
0.4%
40.535767351
0.4%
32.379277171
0.4%
29.156541331
0.4%
8.0862357121
0.4%

2008
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)100.0%
Missing238
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean24.07104107
Minimum1.697783938
Maximum54.743129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:29.394744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.697783938
5-th percentile2.30710855
Q17.133736982
median14.60171333
Q345.26100895
95-th percentile51.75240088
Maximum54.743129
Range53.04534506
Interquartile range (IQR)38.12727196

Descriptive statistics

Standard deviation19.54115677
Coefficient of variation (CV)0.8118118661
Kurtosis-1.708148019
Mean24.07104107
Median Absolute Deviation (MAD)12.21648589
Skewness0.3406864293
Sum673.9891501
Variance381.856808
MonotonicityNot monotonic
2022-04-02T14:28:29.508443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
52.4433571
 
0.4%
37.424639131
 
0.4%
34.621125461
 
0.4%
8.6347675461
 
0.4%
48.720306111
 
0.4%
5.69773761
 
0.4%
46.439351721
 
0.4%
10.475365941
 
0.4%
16.258891411
 
0.4%
6.7690335941
 
0.4%
Other values (18)18
 
6.8%
(Missing)238
89.5%
ValueCountFrequency (%)
1.6977839381
0.4%
2.1248311421
0.4%
2.6456237371
0.4%
4.7910530781
0.4%
5.3256897951
0.4%
5.69773761
0.4%
6.7690335941
0.4%
7.2553047771
0.4%
8.0707938251
0.4%
8.2173613281
0.4%
ValueCountFrequency (%)
54.7431291
0.4%
52.4433571
0.4%
50.469196661
0.4%
49.99086151
0.4%
48.720306111
0.4%
46.439351721
0.4%
45.93880831
0.4%
45.035075831
0.4%
43.085328581
0.4%
37.424639131
0.4%

2009
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct29
Distinct (%)100.0%
Missing237
Missing (%)89.1%
Infinite0
Infinite (%)0.0%
Mean19.39679215
Minimum0.8610915845
Maximum51.28400186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:29.610168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.8610915845
5-th percentile0.9832655512
Q15.115791305
median9.720193986
Q335.86389783
95-th percentile48.14618645
Maximum51.28400186
Range50.42291028
Interquartile range (IQR)30.74810652

Descriptive statistics

Standard deviation17.75936872
Coefficient of variation (CV)0.9155827721
Kurtosis-1.321715658
Mean19.39679215
Median Absolute Deviation (MAD)8.751530387
Skewness0.5630549166
Sum562.5069722
Variance315.3951774
MonotonicityNot monotonic
2022-04-02T14:28:29.713890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
51.284001861
 
0.4%
0.86109158451
 
0.4%
33.121081751
 
0.4%
1.2882738931
 
0.4%
14.809852531
 
0.4%
49.715477411
 
0.4%
6.346946781
 
0.4%
6.6824792411
 
0.4%
45.792250011
 
0.4%
1.0051684791
 
0.4%
Other values (19)19
 
7.1%
(Missing)237
89.1%
ValueCountFrequency (%)
0.86109158451
0.4%
0.96866359961
0.4%
1.0051684791
0.4%
1.2882738931
0.4%
1.3057991341
0.4%
2.3483311861
0.4%
4.5046902751
0.4%
5.1157913051
0.4%
5.6141994531
0.4%
6.346946781
0.4%
ValueCountFrequency (%)
51.284001861
0.4%
49.715477411
0.4%
45.792250011
0.4%
45.723745741
0.4%
41.866865651
0.4%
41.256161611
0.4%
40.885036911
0.4%
35.863897831
0.4%
33.121081751
0.4%
28.189538661
0.4%

2010
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32
Distinct (%)100.0%
Missing234
Missing (%)88.0%
Infinite0
Infinite (%)0.0%
Mean20.75451767
Minimum0.6619918201
Maximum58.62821441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:29.818582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6619918201
5-th percentile0.9432347612
Q14.110016981
median12.68583098
Q337.57698048
95-th percentile54.53917916
Maximum58.62821441
Range57.96622259
Interquartile range (IQR)33.4669635

Descriptive statistics

Standard deviation19.49573968
Coefficient of variation (CV)0.9393492054
Kurtosis-1.108642622
Mean20.75451767
Median Absolute Deviation (MAD)11.67755707
Skewness0.622664754
Sum664.1445653
Variance380.0838656
MonotonicityNot monotonic
2022-04-02T14:28:29.919344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
42.928510941
 
0.4%
3.4132036361
 
0.4%
14.092349151
 
0.4%
36.468748451
 
0.4%
0.96397474961
 
0.4%
33.487190441
 
0.4%
58.628214411
 
0.4%
5.6525725891
 
0.4%
0.91788588651
 
0.4%
5.9048338671
 
0.4%
Other values (22)22
 
8.3%
(Missing)234
88.0%
ValueCountFrequency (%)
0.66199182011
0.4%
0.91788588651
0.4%
0.96397474961
0.4%
1.0525730691
0.4%
1.4986919121
0.4%
1.757999591
0.4%
2.7388723491
0.4%
3.4132036361
0.4%
4.3422880961
0.4%
5.2589612161
0.4%
ValueCountFrequency (%)
58.628214411
0.4%
57.512930131
0.4%
52.106110191
0.4%
50.511687041
0.4%
45.368421031
0.4%
42.928510941
0.4%
42.389513511
0.4%
40.901676591
0.4%
36.468748451
0.4%
33.487190441
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)100.0%
Missing232
Missing (%)87.2%
Infinite0
Infinite (%)0.0%
Mean22.12949409
Minimum1.240626354
Maximum56.34706675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:30.023128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.240626354
5-th percentile2.106957811
Q16.175477726
median15.97030275
Q336.03760007
95-th percentile52.9025249
Maximum56.34706675
Range55.1064404
Interquartile range (IQR)29.86212234

Descriptive statistics

Standard deviation18.19771608
Coefficient of variation (CV)0.8223286083
Kurtosis-1.197815486
Mean22.12949409
Median Absolute Deviation (MAD)13.33853942
Skewness0.5028419789
Sum752.402799
Variance331.1568704
MonotonicityNot monotonic
2022-04-02T14:28:30.121868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3.0095753781
 
0.4%
44.953400191
 
0.4%
6.3110129981
 
0.4%
47.221082361
 
0.4%
8.3174408451
 
0.4%
5.5717184631
 
0.4%
2.4779717921
 
0.4%
14.47814381
 
0.4%
17.462461691
 
0.4%
34.149210051
 
0.4%
Other values (24)24
 
9.0%
(Missing)232
87.2%
ValueCountFrequency (%)
1.2406263541
0.4%
1.4179318471
0.4%
2.4779717921
0.4%
2.4940934581
0.4%
2.6550399731
0.4%
3.0095753781
0.4%
3.8526825661
0.4%
5.5717184631
0.4%
6.1302993021
0.4%
6.3110129981
0.4%
ValueCountFrequency (%)
56.347066751
0.4%
52.944893721
0.4%
52.879710921
0.4%
51.869915171
0.4%
47.221082361
0.4%
44.953400191
0.4%
40.223054281
0.4%
38.447657591
0.4%
36.194308421
0.4%
35.567475021
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)100.0%
Missing227
Missing (%)85.3%
Infinite0
Infinite (%)0.0%
Mean23.46822849
Minimum0.3709714256
Maximum59.52041378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:30.227575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3709714256
5-th percentile0.8642284303
Q17.369507841
median17.24912026
Q342.42641032
95-th percentile51.31373631
Maximum59.52041378
Range59.14944236
Interquartile range (IQR)35.05690248

Descriptive statistics

Standard deviation18.70539534
Coefficient of variation (CV)0.7970518673
Kurtosis-1.418797194
Mean23.46822849
Median Absolute Deviation (MAD)15.91393812
Skewness0.3082936234
Sum915.2609111
Variance349.8918148
MonotonicityNot monotonic
2022-04-02T14:28:30.334839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
36.255063151
 
0.4%
46.600474071
 
0.4%
43.279278861
 
0.4%
18.791511661
 
0.4%
7.3384099771
 
0.4%
1.2460628841
 
0.4%
17.249120261
 
0.4%
12.225064871
 
0.4%
43.788429621
 
0.4%
14.220700491
 
0.4%
Other values (29)29
 
10.9%
(Missing)227
85.3%
ValueCountFrequency (%)
0.37097142561
0.4%
0.71224091621
0.4%
0.88111593181
0.4%
1.2460628841
0.4%
1.3351821471
0.4%
1.5996211171
0.4%
2.2843161271
0.4%
5.4771515031
0.4%
5.682549671
0.4%
7.3384099771
0.4%
ValueCountFrequency (%)
59.520413781
0.4%
53.362759381
0.4%
51.086067081
0.4%
47.925470641
0.4%
46.957633921
0.4%
46.859933931
0.4%
46.600474071
0.4%
43.788429621
0.4%
43.279278861
0.4%
43.212258631
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)100.0%
Missing242
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean25.70074002
Minimum1.019430401
Maximum56.39749137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:30.435869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.019430401
5-th percentile1.996383142
Q18.240849619
median23.88785244
Q344.02224433
95-th percentile52.73562317
Maximum56.39749137
Range55.37806097
Interquartile range (IQR)35.78139471

Descriptive statistics

Standard deviation19.1837315
Coefficient of variation (CV)0.7464272034
Kurtosis-1.652124488
Mean25.70074002
Median Absolute Deviation (MAD)17.77879612
Skewness0.1559840098
Sum616.8177604
Variance368.0155541
MonotonicityNot monotonic
2022-04-02T14:28:30.525117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1.0194304011
 
0.4%
51.058920081
 
0.4%
36.529737391
 
0.4%
14.368704841
 
0.4%
56.397491371
 
0.4%
10.784430641
 
0.4%
5.0485886341
 
0.4%
11.147379271
 
0.4%
11.493083191
 
0.4%
17.185147071
 
0.4%
Other values (14)14
 
5.3%
(Missing)242
91.0%
ValueCountFrequency (%)
1.0194304011
0.4%
1.869138991
0.4%
2.7174333381
0.4%
5.0485886341
0.4%
6.0080077021
0.4%
6.210104931
0.4%
8.9177645161
0.4%
10.784430641
0.4%
11.147379271
0.4%
11.493083191
0.4%
ValueCountFrequency (%)
56.397491371
0.4%
53.031511951
0.4%
51.058920081
0.4%
48.687496151
0.4%
45.530416371
0.4%
44.394747671
0.4%
43.898076551
0.4%
39.349747551
0.4%
36.529737391
0.4%
35.579580661
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)100.0%
Missing236
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean18.91479325
Minimum1.086725533
Maximum54.75133382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:30.621828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.086725533
5-th percentile1.329154439
Q14.360801467
median9.616393527
Q335.68988701
95-th percentile53.71973158
Maximum54.75133382
Range53.66460828
Interquartile range (IQR)31.32908555

Descriptive statistics

Standard deviation18.74619234
Coefficient of variation (CV)0.9910862939
Kurtosis-0.8000063332
Mean18.91479325
Median Absolute Deviation (MAD)6.235964535
Skewness0.9149614714
Sum567.4437975
Variance351.4197273
MonotonicityNot monotonic
2022-04-02T14:28:30.731534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
47.93254481
 
0.4%
15.249243271
 
0.4%
1.2931994231
 
0.4%
37.105730651
 
0.4%
6.1561113571
 
0.4%
54.671697741
 
0.4%
5.4831616041
 
0.4%
11.515864851
 
0.4%
17.882776971
 
0.4%
13.708039171
 
0.4%
Other values (20)20
 
7.5%
(Missing)236
88.7%
ValueCountFrequency (%)
1.0867255331
0.4%
1.2931994231
0.4%
1.3730994571
0.4%
2.1861735711
0.4%
3.3527477311
0.4%
3.4081102521
0.4%
3.8087272131
0.4%
3.9866814211
0.4%
5.4831616041
0.4%
6.1561113571
0.4%
ValueCountFrequency (%)
54.751333821
0.4%
54.671697741
0.4%
52.556217371
0.4%
47.93254481
0.4%
47.144803031
0.4%
43.006368441
0.4%
41.889060681
0.4%
37.105730651
0.4%
31.442356111
0.4%
17.882776971
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct34
Distinct (%)100.0%
Missing232
Missing (%)87.2%
Infinite0
Infinite (%)0.0%
Mean20.92559313
Minimum0.9236530166
Maximum54.04975725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:30.838273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.9236530166
5-th percentile1.371115283
Q15.262259197
median10.51683598
Q339.25590588
95-th percentile52.55886935
Maximum54.04975725
Range53.12610423
Interquartile range (IQR)33.99364669

Descriptive statistics

Standard deviation19.48220627
Coefficient of variation (CV)0.9310228939
Kurtosis-1.333196514
Mean20.92559313
Median Absolute Deviation (MAD)7.979127022
Skewness0.62297547
Sum711.4701663
Variance379.5563612
MonotonicityNot monotonic
2022-04-02T14:28:30.948981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
43.91650171
 
0.4%
3.8515570651
 
0.4%
2.7070688991
 
0.4%
5.9351065211
 
0.4%
18.000744791
 
0.4%
10.376314761
 
0.4%
8.9221494581
 
0.4%
5.2946666151
 
0.4%
28.67696331
 
0.4%
54.049757251
 
0.4%
Other values (24)24
 
9.0%
(Missing)232
87.2%
ValueCountFrequency (%)
0.92365301661
0.4%
1.188257291
0.4%
1.4695772791
0.4%
2.3683490181
0.4%
2.7070688991
0.4%
2.7414647941
0.4%
3.7678166131
0.4%
3.8515570651
0.4%
5.2514567241
0.4%
5.2946666151
0.4%
ValueCountFrequency (%)
54.049757251
0.4%
53.418428811
0.4%
52.096029641
0.4%
51.422163061
0.4%
50.612097191
0.4%
49.690612311
0.4%
44.912702091
0.4%
43.91650171
0.4%
40.16295671
0.4%
36.534753441
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing235
Missing (%)88.3%
Infinite0
Infinite (%)0.0%
Mean20.07316477
Minimum0.4964287682
Maximum56.34119953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:31.053697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4964287682
5-th percentile0.5678248133
Q15.337885112
median10.75719945
Q335.71264961
95-th percentile51.1040736
Maximum56.34119953
Range55.84477076
Interquartile range (IQR)30.3747645

Descriptive statistics

Standard deviation18.89280137
Coefficient of variation (CV)0.9411969454
Kurtosis-1.139941935
Mean20.07316477
Median Absolute Deviation (MAD)7.939611305
Skewness0.6997284133
Sum622.268108
Variance356.9379436
MonotonicityNot monotonic
2022-04-02T14:28:31.153432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
34.67822921
 
0.4%
50.316391881
 
0.4%
0.56960127121
 
0.4%
36.060276861
 
0.4%
5.7141106191
 
0.4%
5.2414042881
 
0.4%
56.341199531
 
0.4%
43.438294911
 
0.4%
3.6276772251
 
0.4%
5.4343659351
 
0.4%
Other values (21)21
 
7.9%
(Missing)235
88.3%
ValueCountFrequency (%)
0.49642876821
0.4%
0.56604835531
0.4%
0.56960127121
0.4%
0.9505732991
0.4%
3.4570861151
0.4%
3.6276772251
0.4%
4.4061300051
0.4%
5.2414042881
0.4%
5.4343659351
0.4%
5.7141106191
0.4%
ValueCountFrequency (%)
56.341199531
0.4%
51.891755331
0.4%
50.316391881
0.4%
49.987949481
0.4%
47.657312391
0.4%
44.522333421
0.4%
43.438294911
0.4%
36.060276861
0.4%
35.365022361
0.4%
34.67822921
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)100.0%
Missing240
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean18.70752254
Minimum0.8929414645
Maximum56.38057804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:31.253137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.8929414645
5-th percentile1.711900214
Q15.36138486
median10.44131083
Q331.21458573
95-th percentile53.95939644
Maximum56.38057804
Range55.48763658
Interquartile range (IQR)25.85320087

Descriptive statistics

Standard deviation17.81378245
Coefficient of variation (CV)0.9522256308
Kurtosis-0.3280170055
Mean18.70752254
Median Absolute Deviation (MAD)7.456820461
Skewness0.9933396805
Sum486.3955861
Variance317.3308453
MonotonicityNot monotonic
2022-04-02T14:28:31.352012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7.001909281
 
0.4%
35.232607891
 
0.4%
3.5039956661
 
0.4%
5.5168122131
 
0.4%
56.380578041
 
0.4%
5.3959105131
 
0.4%
10.423826281
 
0.4%
19.499134861
 
0.4%
19.076260211
 
0.4%
5.3498763091
 
0.4%
Other values (16)16
 
6.0%
(Missing)240
90.2%
ValueCountFrequency (%)
0.89294146451
0.4%
1.1603802661
0.4%
3.3664600581
0.4%
3.4074811091
0.4%
3.5039956661
0.4%
4.7188501651
0.4%
5.3498763091
0.4%
5.3959105131
0.4%
5.5168122131
0.4%
7.001909281
0.4%
ValueCountFrequency (%)
56.380578041
0.4%
55.057368921
0.4%
50.665478991
0.4%
45.671064261
0.4%
35.362812781
0.4%
35.232607891
0.4%
31.521407351
0.4%
30.294120861
0.4%
19.499134861
0.4%
19.076260211
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)100.0%
Missing239
Missing (%)89.8%
Infinite0
Infinite (%)0.0%
Mean18.46395028
Minimum0.9075715336
Maximum52.32998443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:31.456337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.9075715336
5-th percentile1.525229
Q15.426526712
median11.09690111
Q332.38969914
95-th percentile49.5098857
Maximum52.32998443
Range51.4224129
Interquartile range (IQR)26.96317243

Descriptive statistics

Standard deviation17.03425695
Coefficient of variation (CV)0.9225683933
Kurtosis-0.6602353055
Mean18.46395028
Median Absolute Deviation (MAD)8.396462863
Skewness0.8722028345
Sum498.5266577
Variance290.1659098
MonotonicityNot monotonic
2022-04-02T14:28:31.563617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3.6321746591
 
0.4%
35.134000221
 
0.4%
49.764291351
 
0.4%
37.822778451
 
0.4%
5.8194618551
 
0.4%
0.90757153361
 
0.4%
5.3311318181
 
0.4%
10.12061181
 
0.4%
20.802879491
 
0.4%
6.4479741591
 
0.4%
Other values (17)17
 
6.4%
(Missing)239
89.8%
ValueCountFrequency (%)
0.90757153361
0.4%
1.253517091
0.4%
2.1592234571
0.4%
2.7004382471
0.4%
3.3052290431
0.4%
3.6321746591
0.4%
5.3311318181
0.4%
5.5219216061
0.4%
5.8194618551
0.4%
6.4479741591
0.4%
ValueCountFrequency (%)
52.329984431
0.4%
49.764291351
0.4%
48.916272531
0.4%
47.828539931
0.4%
37.822778451
0.4%
35.134000221
0.4%
33.909850471
0.4%
30.869547811
0.4%
20.802879491
0.4%
20.119986391
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)100.0%
Missing250
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean18.815784
Minimum4.80744769
Maximum47.47545194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-02T14:28:31.658366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.80744769
5-th percentile5.4421272
Q18.081247098
median12.0053116
Q331.45444581
95-th percentile39.74834897
Maximum47.47545194
Range42.66800425
Interquartile range (IQR)23.37319871

Descriptive statistics

Standard deviation13.70151095
Coefficient of variation (CV)0.7281924022
Kurtosis-0.6763752622
Mean18.815784
Median Absolute Deviation (MAD)6.350408242
Skewness0.8154540851
Sum301.0525439
Variance187.7314023
MonotonicityNot monotonic
2022-04-02T14:28:31.741516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10.858291921
 
0.4%
34.939191671
 
0.4%
37.172647981
 
0.4%
6.7144774741
 
0.4%
5.6536870361
 
0.4%
10.041071371
 
0.4%
21.409877991
 
0.4%
13.152331291
 
0.4%
34.300337851
 
0.4%
6.3096928211
 
0.4%
Other values (6)6
 
2.3%
(Missing)250
94.0%
ValueCountFrequency (%)
4.807447691
0.4%
5.6536870361
0.4%
6.3096928211
0.4%
6.7144774741
0.4%
8.5368369721
0.4%
10.041071371
0.4%
10.82088131
0.4%
10.858291921
0.4%
13.152331291
0.4%
18.354503521
0.4%
ValueCountFrequency (%)
47.475451941
0.4%
37.172647981
0.4%
34.939191671
0.4%
34.300337851
0.4%
30.505815131
0.4%
21.409877991
0.4%
18.354503521
0.4%
13.152331291
0.4%
10.858291921
0.4%
10.82088131
0.4%

2020
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

Interactions

2022-04-02T14:28:24.132159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:03.464359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:05.128725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:06.514419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:07.852185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:09.365836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:10.699230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:11.977931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:13.497424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:14.786319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:16.065682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:17.542717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:18.834478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:20.104820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:21.596424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:22.856176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:24.223914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:03.547170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:05.219454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:06.605747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:07.939953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:09.460577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:10.781976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:12.066266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:13.589209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:14.873181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:16.153471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:17.635492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:18.927230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:20.192586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:21.674248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:22.948929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:24.520150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:03.643880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:05.301264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:06.698065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:08.030733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:09.550543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:10.860793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:12.147050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:13.674980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:14.954931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:16.237252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:17.718276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:19.005937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:20.276417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:21.752066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-02T14:28:14.700550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:15.989887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:17.469940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:18.764103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:20.031046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:21.522616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:22.781958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-02T14:28:24.058055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-02T14:28:32.120855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-02T14:28:32.887875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-02T14:28:33.653943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-02T14:28:34.349749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-02T14:28:25.975080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-02T14:28:26.683372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-02T14:28:27.058944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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